Iris image segmentation based on approximate methods with subsequent refinements

Gankin, Konstantin; Gneushev, A. N.; Матвеев, И. А.

doi:10.1134/s1064230714020099

Cited by 17 publications

(5 citation statements)

References 16 publications

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“…Many works are devoted to the specific problem of iris detection, for example [10][11][12][13][14][15]. At the input, the information of the original image is taken without prior hierarchical or other ordering.…”

Section: Methods Demonstrationmentioning

confidence: 99%

Example-Based Object Detection in the Attached Image

Kharinov

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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The paper solves the problem of detecting exemplified objects in a color image. A solution provides the representation of similar objects in the same colors, and different objects in different colors. This is achieved by combining images of object examples and a target image into a single joint image, which is represented in sequential number 1, 2, ..., etc. colors. The mentioned effect is demonstrated by detecting irises and pupils in a test image. It is explained by the fact that: a) the joint image is approximated by a hierarchy of approximations in sequential color numbers; b) the hierarchy of approximations is described by a convex sequence of approximation errors (values of the total squared error ); c) due to the convexity, the approximation errors are reduced for all approximations of the joint image. In the last explanation item, it is applied the operation of combining hierarchically organized objects into a single object, which is introduced in this paper. To produce the required hierarchy of image approximations Ward's pixel clustering is used. Ward's method is generalized for image processing by parts (within pixel subsets) that provides generation of multiple proper approximation hierarchies and accelerates the calculations. To do so, the so- called split-and-merge pixel cluster CI-method is embedded into Ward's generalized method to provide a real-life minimization of the error for image approximation in a fixed number of colors.

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Section: Methods Demonstrationmentioning

confidence: 99%

Example-Based Object Detection in the Attached Image

Kharinov

2022

Proceedings of the 32nd International Conference on Computer Graphics and Vision

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“…where r pupil (t) and r iris (t) are measured radii or pupil and iris in the moment t. One should note that it is necessary to measure iris radius in all frames of the sequence since the testee can move and scale of image may change. Radii of pupil and iris are determined in frames by the iris segmentation algorithms (Gankin et al, 2014) that give higher precision compared to photometric methods (Shahnovich, 1964) and substantially higher speed compared to manual processing (Velhover and Ananin, 1991), which were used earlier.…”

Section: Binocular Pupillometry Methodsmentioning

confidence: 99%

Monitoring of the Functional State of Athletes by Pupillometry

Varchenko¹,

Gankin²,

Матвеев

2014

Proceedings of the 2nd International Congress on Sports Sciences Research and Technology Support

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Method of binocular pupillometry is presented with an application to evaluation of the functional state of athletes. The method is based on synchronous registration of both pupil reactions to a light flash stimulus. Pupil reaction reflects the state of sympathetic-parasympathetic balance of autonomic nervous system and serves as an objective measurement of the body condition. The advantages of the method are: non-invasiveness, quick operation, wide spectrum of measured characteristics, and the fact that pupil reaction to light flash stimulus is an unconditioned reflex and is not controlled by the cortex and consciousness. Results of the experiments performed with various groups of athletes are presented. A possibility of using the pupillometry for evaluating athletes' state is shown. Comparison with traditional methods of functional state evaluation is done.

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“…With the development of the X-ray imaging techniques, quantitative phase information can be recovered from a set of X-ray phase contrast micro-CT data, and several segmentation means have been used to extract the interested characteristic microstructures of CMMs [69][70][71][72]. Ye et al have moved from 3D imaging to quantitative analysis [66].…”

Section: Quantitative Analysis Of 3d Characteristic Microstructuresmentioning

confidence: 99%

Microscopic identification of Chinese medicinal materials based on X-ray phase contrast imaging: from qualitative to quantitative

et al. 2016

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JINST 11 C07001phase contrast micro-CT for investigating the characteristic microstructures of CMMs. During this period, a variety of typical CMMs have been investigated, from two-dimensional (2D) radiography to three-dimensional (3D) micro-CT, from qualitative to quantitative. Taken together, these results verify that quantitative X-ray phase contrast micro-CT is a practical tool for the microscopic investigation of CMMs. Additionally, further efforts are being made to find the relationship between the microstructures' quantitative factors and active chemical components. At present, experimental results have demonstrated that the quality of CMMs can be evaluated directly via the quantitative information of their characteristic microstructures. K: Computerized Tomography (CT) and Computed Radiography (CR); X-ray radiography and digital radiography (DR)

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Iris image segmentation based on approximate methods with subsequent refinements

Cited by 17 publications

References 16 publications

Example-Based Object Detection in the Attached Image

Example-Based Object Detection in the Attached Image

Monitoring of the Functional State of Athletes by Pupillometry

Microscopic identification of Chinese medicinal materials based on X-ray phase contrast imaging: from qualitative to quantitative

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